Using Bayesian optimization to refine laboratory tank sound propagation modeling

Abstract

Because ocean measurements are costly, a laboratory water tank provides opportunities to take scaled measurements and develop approaches to machine learning that are transferable to ocean acoustic problems. These applications are better achieved if reflections off the side walls are significantly reduced. To reduce these reflections, the side walls of the tank are lined with Aptile SF5048 echo reducing tiles made by Precision Acoustics. This work studies the extent to which two ocean sound propagation models (namely ORCA and BELLHOP) can predict relative transmission loss as a function of source-receiver range and frequency. The modeling parameters of sound speed and density that determine the reflections at the bottom of the tank were refined through a Bayesian optimization algorithm. We found that the optimized parameters improve the model-data agreement and that the models fit the data at certain frequencies corresponding mainly to peaks in the echo reduction for the Aptile panels. This agreement implies that at certain frequencies, sound propagation in our tank acts as it would in a large body of water. Using our tank as a testbed for developing machine learning algorithms is a reasonable approach.